Scarlet Stadtler

Scarlet Stadtler
Forschungszentrum Jülich · Institute for Advanced Simulation (IAS)

Doctor of Philosophy

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19
Publications
4,143
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282
Citations

Publications

Publications (19)
Article
Full-text available
Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a hi...
Preprint
Full-text available
Numerical weather prediction (NWP) models solve a system of partial differential equations based on physical laws to forecast the future state of the atmosphere. These models are deployed operationally, but they are computationally very expensive. Recently, the potential of deep neural networks to generate bespoken weather forecasts has been explor...
Article
Full-text available
Air quality is relevant to society because it poses environmental risks to humans and nature. We use explainable machine learning in air quality research by analyzing model predictions in relation to the underlying training data. The data originate from worldwide ozone observations, paired with geospatial data. We use two different architectures: a...
Article
Full-text available
Artificial intelligence for air quality IntelliAQ is an ERC Advanced Grant project to explore the application of cutting-edge machine learning techniques to global air quality data in combination with high resolution geospatial and weather data. It combines novel data management and data science approaches to build the foundation for innovative air...
Preprint
Full-text available
Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here we present a data-driven ozone mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a hig...
Chapter
In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the Jülich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligen...
Preprint
Full-text available
In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the J\"ulich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intellig...
Article
Full-text available
With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air...
Presentation
Full-text available
Through the availability of multi-year ground based ozone observations on a global scale, substantial geospatial meta data, and high performance computing capacities, it is now possible to use machine learning for a global data-driven ozone assessment. In this presentation, we will show a novel, completely data-driven approach to map tropospheric o...
Article
Full-text available
The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mi...
Preprint
Full-text available
With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air...
Article
Full-text available
In this paper, we present the implementation and evaluation of the aerosol microphysics module SALSA2.0 in the framework of the aerosol–chemistry–climate model ECHAM-HAMMOZ. It is an alternative microphysics module to the default modal microphysics scheme M7 in ECHAM-HAMMOZ. The SALSA2.0 implementation within ECHAM-HAMMOZ is evaluated against obser...
Article
Full-text available
Within the framework of the global chemistry climate model ECHAM–HAMMOZ, a novel explicit coupling between the sectional aerosol model HAM-SALSA and the chemistry model MOZ was established to form isoprene-derived secondary organic aerosol (iSOA). Isoprene oxidation in the chemistry model MOZ is described by a semi-explicit scheme consisting of 147...
Article
Full-text available
The chemistry–climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parameterizations of aerosols using either a modal scheme (M7) or a bin scheme (SALSA). This article describes and evaluates the model version ECHAM6.3-HAM2.3-MOZ1.0 with a focus on the tropospheric g...
Article
Full-text available
In this paper, we present the implementation and evaluation of the aerosol microphysics module SALSA2.0 in the framework of the aerosol-chemistry-climate model ECHAM-HAMMOZ. It is an alternative microphysics module to the default modal microphysics scheme M7 in ECHAM-HAMMOZ. The SALSA2.0 implementation is evaluated against the observations of aeros...
Article
Full-text available
The impact of six heterogeneous gas–aerosol uptake reactions on tropospheric ozone and nitrogen species was studied using two chemical transport models, the Meteorological Synthesizing Centre-West of the European Monitoring and Evaluation Programme (EMEP MSC-W) and the European Centre Hamburg general circulation model combined with versions of the...
Article
Full-text available
The chemistry climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parametrisations of aerorols using either a modal scheme (M7) or a bin scheme (SALSA). This article describes and evaluates the model version ECHAM6.3-HAM2.3-MOZ1.0 with a focus on the tropospheric ga...
Article
Full-text available
Within the framework of the global chemistry climate model ECHAM-HAMMOZ a novel explicit coupling between the sectional aerosol model HAM-SALSA and the chemistry model MOZ was established to form isoprene derived secondary organic aerosol (iSOA). Isoprene oxidation in the chemistry model MOZ is described by a semi-explicit scheme consisting of 147...
Article
Full-text available
The impact of six heterogeneous gas-aerosol uptake reactions on tropospheric ozone and nitrogen species was studied using two chemical transport models, EMEP MSC-W and ECHAM-HAMMOZ. Species undergoing heterogeneous reactions in both models include N2O5, NO3, NO2, O3, HNO3 and HO2. Since heterogeneous reactions take place at the aerosol surface area...

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